Method and system for determining a period of interest using multiple inputs

ABSTRACT

One or more techniques are provided for identifying a period of minimal motion for an organ of interest, such as the heart or lungs. Motion data is acquired for the organ of interest and for one or more proximate organs using sensor-based and/or image-based techniques. The sensor-based techniques may include electrical and non-electrical techniques. The image-based techniques may include both pre-acquisition and acquisition image data. The motion data for the organ of interest and proximate organs may be used to generate a set of multi-input motion data that may be processed to identify desired periods, such as periods of minimal motion, within the overall motion of the organ of interest.

BACKGROUND OF THE INVENTION

The present technique relates generally to the measurement of theoverall motion undergone by an object. More specifically, the presenttechnique relates to identifying a period of interest in the motion ofan object within a complex system using multiple sources.

Both living and mechanical complex systems may be formed from thecombination of an assortment of components. In the case of livingsystems, the components may be the various organs and tissues thatcomprise the body of an organism. In the case of a mechanical system,the components may be the various parts forming the working mechanism ofthe system. In these types of complex systems a variety of thecomponents may be located on the interior of the system, such as withinthe organism or mechanism body, where they are not subject to easyobservation or examination.

Furthermore, the interior components may move relative to one anotherand to the exterior of the system. For example, in the context of aliving organism, organs, such as the heart, lungs, diaphragm, stomach,and so forth, may move independent of one another (such as the heart andstomach) or in conjunction with one another (such as the lungs anddiaphragm). Similarly, mechanical complex system may have interiormoving parts, such as rotors, turbines, levers, arms, pistons, valves,and so forth, which, depending on the mechanism of operation, may moveindependent of or in conjunction with one another.

The difficulty in observing the interior components of such movingsystems may make it difficult to observe the synchrony, or lack thereof,of the moving components. Furthermore, as one might expect, the motionof one interior component may contribute to the overall motion ofconnected or proximate interior components. The overall motion of acomponent may be difficult to ascertain without knowing the motion ofall possible contributors to the overall motion. For example, in thecontext of the living organism, the motion of the heart may be ofinterest for various reasons, including diagnostic imaging orinterventional procedures. The overall motion of the heart, however, maynot be simply attributable to cardiac contractions but may also beattributable to respiration, i.e., lung and diaphragm motion, toskeletal muscular contractions, or to other proximate muscular motions.Similarly, within a mechanical system, the overall motion of acomponent, such as a turbine, may be attributable not only to the motionof the component itself but may also be attributable to other proximatemoving components which are operating independent of or in conjunctionwith the component of interest.

In some cases, the overall motion of a component may be of interest, notsimply the motion attributable to the component. For example, in amechanical system, the overall motion of an internal component mayindicate a problem, such as a component moving outside of the tolerancerange, or a pending problem or failure, such as a vibration or stressfulmotion indicating the pending breakage or failure of a component.Similarly, in a living organism, the overall motion of an organ, such asthe heart or lungs, may be of interest for imaging or interventionalpurposes, such as to perform motion correction or artifact correction.

Determining the overall motion of a component may be difficult, however.In particular, sensors or component specific information sources mayonly provide information about one aspect of the overall motion of acomponent. For example, in the case of a turbine, internal sensors mayonly report a measure, such as RPM, which provides information about therotation of the turbine, but no information about proximate movingcomponents that may be moving the turbine incidental to their ownmotion.

Similarly, in the context of a living organism, it may be desirable toknow the overall motion of the heart. Techniques such aselectrocardiography (ECG), however, only provide information regardingthe cardiac phase of the heart, i.e., what state of contraction theheart is in at a point in time. Information such as the motion of therespiratory organs, i.e., the lungs and/or diaphragm, which maycontribute to the overall motion of the heart, is not captured by atechnique, such as ECG, which simply ascertains information about thecontractions of the heart. It may, therefore, be desirable tocharacterize the overall motion undergone by an internal component of acomplex system for analysis of the system.

BRIEF DESCRIPTION OF THE INVENTION

The present invention is directed to a technique for a period ofinterest in the overall motion of one or more component of a complexsystem. The overall motion of the component or components of interestmay be determined from a set of multi-input motion data. The multi-inputmotion data may comprise motion data for the component of interestderived from multiple sources, such as sensor-based or image data-basedsources. The multi-input motion data may also comprise motion data forproximate components, which contribute to the overall motion of thecomponent of interest, derived from one or more sources, includingpossibly a source used to measure the component of the organ -ofinterest. The multi-input nature of the multi-input motion data,therefore, may describe either the presence of motion data for more thanone component, the presence of multiple sources of motion data for acomponent, or the presence of both multi-input and multi-source motiondata.

The multi-input motion data may be used to determine one or more periodsof interest for the component of interest, such as a quiescent periodcorresponding to an interval of minimal overall motion for the organ. Aninterval such as a quiescent period may be used, such as in a medicalimaging context, to determine gating points that may used to gate imagedata, prospectively and/or retrospectively, to reduce motion artifactsin the resulting image. In addition, a quiescent period or other periodof interest may be used to derive one or more motion compensationfactors which may be applied during image processing to reduce motionartifacts. Other periods of interest may include a particular phase ofmotion associated with one or more motion cycles or periods, such as theonset of cardiac contraction in medical imaging.

In accordance with one aspect of the present invention, a technique isprovided for identifying one or more periods of minimal motion for aheart. In view of this aspect, at least one set of electrical datarepresentative of cardiac motion and at least one set of non-electricaldata representative of cardiac motion may be acquired. A set ofmulti-input motion data comprising the sets of electrical andnon-electrical data may be generated. One or more periods of minimalmotion for the heart may be extracted from the set of multi-input motiondata. Systems and computer programs that afford functionality of thetype defined by this method are also provided by the present technique.

In accordance with another aspect of the present invention, a techniqueis provided for identifying one or more periods of minimal motion. Inview of this aspect, at least one set of non-electrical datarepresentative of cardiac motion and one or more sets of datarepresentative of respiratory motion may be acquired. A set ofmulti-input motion data comprising the set non-electrical datarepresentative of cardiac motion and the one or more sets of motion datarepresentative of respiratory motion may be generated. One or moreperiods of minimal motion for one of a heart and a respiratory organ maybe extracted from the set of multi-input motion data. Systems andcomputer programs that afford functionality of the type defined by thismethod are also provided by the present technique.

In accordance with a further aspect of the present invention, atechnique is provided for identifying one or more periods of minimalmotion. In view of this aspect, at least one set of electrical datarepresentative of cardiac motion, at least one set of non-electricaldata representative of cardiac motion, and one or more sets of datarepresentative of respiratory motion may be acquired. A set ofmulti-input motion data comprising the set of electrical datarepresentative of cardiac motion, the set non-electrical datarepresentative of cardiac motion, and the one or more sets of motiondata representative of respiratory motion may be generated. One or moreperiods of minimal motion for one of a heart and a respiratory organ maybe extracted from the set of multi-input motion data. Systems andcomputer programs that afford functionality of the type defined by thismethod are also provided by the present technique.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other advantages and features of the invention willbecome apparent upon reading the following detailed description and uponreference to the drawings in which:

FIG. 1 is a general diagrammatical representation of certain functionalcomponents of an exemplary generic imaging system capable of gatingand/or motion correction via the present technique;

FIG. 2 is a flowchart depicting the acquisition of a multi-input motiondata set, in accordance with the present technique;

FIG. 3 is an exemplary representation of a multi-input motion data setand of a common gating period derived from the multi-input motion data;and

FIG. 4 is a flowchart depicting the determination of gating pointsand/or motion compensation factors from a set of multi-input motiondata.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

In the field of complex systems, including complex mechanical and livingsystems, it may be desirable to characterize the motion of one or moreinternal components of the system that cannot be directly observed.Often a measure of the intrinsic motion of a component may be available,such as RPM for a turbine or rotating component or ECG for a heart. Suchmeasures, however, may not convey the overall motion of the component ofinterest, but may instead convey only a part of the overall motion, thatpart attributable to the component itself. The present technique isdirected to measuring the overall motion of an internal component of acomplex mechanical or living system. For the purpose of explanation, anexample of the present technique will be provided in the context ofliving complex systems. In particular, the example discusses thetechnique as it may be applied in the field of medical imaging.

In the field of medical imaging, it is often desirable to characterizethe motion of an imaged organ so that the motion may be accounted for toimprove image quality. Measures of the muscular activity of the organ ofinterest, however, may characterize only a portion of the overall motionof the organ relative to the viewer or imaging scanner. In particular,the overall motion may consist of not only the muscular contractions ofthe organ of interest itself, i.e., the intrinsic motion, but alsomotion attributable to the movement of proximate organs. For example,the overall motion of the heart may consist of not only the muscularcontractions of the heart but also the movement of the respiratoryorgans, i.e., the lungs, and/or diaphragm, or other proximate muscularcontractions, voluntary and/or involuntary, of the patient's body.Therefore, in the example of cardiac imaging, it may be desirable tocharacterize the overall motion of the heart relative to the imager, notsimply the motion attributable to the contraction of the heart itself.

For example, the motion of the organ to be imaged may be used in imageacquisition or reconstruction techniques that rely on data gating. Ingeneral, data gating techniques acquire image data (prospective gating)or select acquired image data (retrospective gating) based upon themotion of the organ being imaged, allowing image data to be acquired orselected at or near the desired phase of motion. The acquired orselected image data may then be reconstructed to form images that havefewer artifacts attributable to organ motion.

However, to the extent that the gating process relies only uponinformation related to the muscular contraction or movement of the organof interest, the process may not account for the motion componentattributable to other proximate organs. In other words, the overallmotion of the organ of interest may not be accounted for by looking atthe muscular activity of the organ of interest alone. Failure to accountfor the overall motion of the organ of interest during gating may resultin reconstructed images containing motion related artifacts.

One way in which the overall motion of the organ of interest may bedetermined is to employ one or more motion determination techniquescapable of determining the motion of the organ of interest moreprecisely and/or to determine the motion of one or more proximate organsthat contribute to the overall motion of the organ of interest. Thesemultiple inputs of motion data may be analyzed, such as by variouscombination and/or separation techniques, to determine one or morequiescent periods for the organ of interest in which the overall motionof the organ is minimized relative to the imager. The quiescent periodsmay then be used to gate the imaging process, either prospectively orretrospectively, to generate images of the organ of interest withreduced or minimal motion artifacts.

Alternatively, other desired periods may be selected based upon themultiple inputs. For example, a period may be selected in which themotion of proximate organs, such as the lungs, is minimized, while theheart is at an active phase of the cardiac cycle, such as the initiationof contraction. In this way the, motion affects attributable toproximate organs may be minimized, while a cardiac phase of interest maybe imaged.

Similarly, if multiple motion inputs are used to measure the cardiacmotion, such as electrical and mechanical sensors, a period may beselected based upon specific combination motion inputs. For example, aperiod may be of interest where the electrical activity, measured byelectrical sensors, indicates cardiac muscle depolarization, but themechanical activity, measured as displacement, acceleration, etc., isnot indicative of the expected muscular motion. In this manner, periodsof interest marked by specific or signature characteristics may bepinpointed and imaged or the respective image data selected from alarger set of image data for processing. As one of ordinary skill in theart will appreciate, one or more of the multiple inputs may be derivedfrom the imaging modality itself, such as motion data derived frompre-acquisition imagining sequences, such as Navigator pulses or scoutimages, or from acquisition data, whether in unreconstructed orreconstructed form. In this manner, sensory and/or imaging data may beused to derive the motion data comprising the multiple inputs.

An exemplary imaging system 10 capable of operating in accordance withthe present technique is depicted in FIG. 1. Generally, the imagingsystem 10 includes some type of imager 12 that detects signals andconverts the signals to useful data. As described more fully below, theimager 12 may operate in accordance with various physical principals forcreating the image data. In general, however, the imager 12 createsimage data indicative of the region of interest in a patient 14, eitherin a conventional support, such as photographic film, or in a digitalmedium.

The imager 12 operates under the control of system control circuitry 16.The system control circuitry 16 may include a wide range of circuits,such as radiation source control circuits, timing circuits, circuits forcoordinating data acquisition in conjunction with patient or tablemovements, circuits for controlling the position of radiation sourcesand detectors, and so forth. In the present context, the system controlcircuitry 16 may also include memory elements, such as magnetic oroptical storage media, for storing programs and routines executed by thesystem control circuitry 16 or by associated components of the system10. The stored programs or routines may include programs or routines forperforming all or part of the present technique.

Image data or signals acquired by the imager 12 may be processed by theimager 12, such as for conversion to digital values, and provided todata acquisition circuitry 18. The data acquisition circuitry 18 mayperform a wide range of processing functions, such as adjustment ofdigital dynamic ranges, smoothing or sharpening of data, as well ascompiling of data streams and files, where desired. In situations wherepre-acquisition image data, such as Navigator pulses in magneticresonance imaging (MRI), are acquired, the data acquisition circuitry 18may provide image data to the system control circuitry 16 forprospective gating.

The data acquisition circuitry 18 may also transfer acquisition imagedata to data processing circuitry 20, where additional processing andanalysis are performed. The data processing circuitry 20 may performsubstantial analyses of image data, including ordering, sharpening,smoothing, feature recognition, and so forth. In addition, the dataprocessing circuitry 20 may receive motion data for one or more organsfrom one or more sensor-based motion detection systems 34, as discussedin detail below. Based on image-based and/or sensor-based motion data,gating and/or motion compensation may be facilitated by the dataprocessing circuitry 20, such as by determining gating intervals and/ormotion corrections factors that may be provided to the system controlcircuitry 16 and/or operator workstation 22. The processed image datamay be stored in short or long term storage devices, such as picturearchiving communication systems, which may be located within or remotefrom the imaging system 10 and/or reconstructed and displayed for anoperator, such as at the operator workstation 22.

In addition to displaying the reconstructed image, the operatorworkstation 22 may control the above-described operations and functionsof the imaging system 10, typically via an interface with the systemcontrol circuitry 16. The operator workstation 22 may include one ormore processor-based components, such as general purpose or applicationspecific computers 24. In addition to the processor-based components,the operator workstation 22 may include various memory and/or storagecomponents including magnetic and optical mass storage devices, internalmemory, such as RAM chips. The memory and/or storage components may beused for storing programs and routines for performing the techniquesdescribed herein that are executed by the operator workstation 22 or byassociated components of the system 10. Alternatively, the programs androutines may be stored on a computer accessible storage and/or memoryremote from the operator workstation 22 but accessible by network and/orcommunication interfaces present on the operator workstation 22.

The operator workstation 22 may also comprise various input/output (I/O)interfaces, as well as various network or communication interfaces. Thevarious I/O interfaces may allow communication with user interfacedevices, such as a display 26, keyboard 28, mouse 30, and printer 32,that may be used for viewing and inputting configuration informationand/or for operating the imaging system 10. The various network andcommunication interfaces may allow connection to both local and widearea intranets and storage networks as well as the Internet. The variousI/O and communication interfaces may utilize wires, lines, or suitablewireless interfaces, as appropriate or desired.

As one of ordinary skill in the art will appreciate, more than a singleoperator workstation 22 may be provided for an imaging system 10. Forexample, an imaging scanner or station may include an operatorworkstation 22 which permits regulation of the parameters involved inthe image data acquisition procedure, whereas a different operatorworkstation 22 may be provided for manipulating, enhancing, and viewingresults and reconstructed images.

The motion of one or more organs of interest may be detected and/ormeasured in a variety of ways and provided to the imaging system 10. Asone of ordinary skill in the art will readily apprehend, the type ofdata gating desired may determine the type of motion data acquired. Insome cases, the motion data of interest may be derived using the imagescanner 12 itself. For example, pre-acquisition imaging techniques, suchas navigator pulses in MR systems, scout images in CT systems orfluoroscopic images in other generalized X-ray applications, may beemployed to determine the motion of the organ of interest or otherorgans proximate to the organ of interest. In other cases, motion datafor the organ or organs of interest may be determined from theacquisition image data in the acquired, i.e., unreconstructed, domainand/or in the reconstructed domain. Use of the imaging system 10 toacquire motion data, both in the pre-acquisition and the acquisitioncontext, are examples of image-based motion detection and measurement,as discussed in detail herein.

In some instances, however, sensor-based motion determination techniquesmay be employed. In these instances, the exemplary imaging system 10 mayinclude or may be in communication with one or more sensor-based motiondetermination systems 34. The sensor-based motion determination systems34 typically comprise one or more sensors 36 in the form of a pad orcontact that may be disposed on skin surface of the patient 14. Thecontact area of a sensor 36 may vary in size from micrometers tocentimeters in diameter and height. The size selected is usually basedon an application. Similarly, the number of sensors 36 used may dependon the application.

When disposed on the patient 14, the sensor 36 may detect and/or measuresome metric or parameter of interest, such as an electrical ormechanical event, that may be used as an indicator of organ motion. Thesensor 36 may be connected to the respective sensor-based determinationsystem 34 via one or more leads 38 which may transmit a signalrepresentative of the sensed metric or parameter to the respectivesystem 34 for processing. In some contexts, the sensor 36 maycommunicate with the respective sensor-based motion determination system34 via wireless means, such as a wireless network protocol, as opposedto a physical lead 38.

Sensor-based determination systems 34 may include electrical motiondetermination systems 40, such as systems that detect or measureelectrical activity or characteristics of an organ to indicate motion.Electrical motion determination systems 40 may measure electricalactivity indicative of the muscular contractions of an organ, such as anelectrocardiogram (ECG). Electrical motion detection systems 40 may alsomeasure changes in electrical properties, such as impedance, which areindicative of organ motion, such as impedance plethysmography. Theelectrical sensors 42 used to detect electrical events, such aselectrical contact pads, are typically strategically placed to detectthe electrical attributes of the organ. For example, in the context ofdetecting and monitoring the motion of the heart, electrical events canbe detected by a four-sensor ECG system, a twelve-sensor ECG system, avector cardiography (VCG) type of arrangement, or by multiple ECGsensors arranged in array or matrix format to cover the region ofinterest.

Sensor-based motion determination systems 34 may also includenon-electrical motion determination systems 44, such as systems thatdetect and/or measure displacement, acceleration, velocity, pressure,and/or other mechanical indicators of motion. Various types ofmechanical sensors 46 may be employed to detect and/or measure themechanical indicators of motion, including accelerometers, opticalmarkers, displacement sensors, force sensors, ultrasonic sensors, straingauges, photodiodes, and pressure sensors.

The non-electrical events may be detected one or more mechanical sensors46. In the case of multiple mechanical sensors 46, the mechanicalsensors 46 may be arranged in an array or matrix format placed in ornear the region of interest. Sensor arrays or configurations arepossible in which the mechanical sensors 46 are arranged in athree-dimensional matrix such that the entire body surface in the regionof interest is covered, such as by using a suit or wrap. Typically, inan array of mechanical sensors 46 used to measure non-electrical events,the mechanical sensors 46 are placed equidistant from each other. Forinstance, a δ unit of separation may be maintained between themechanical sensors 46 in the X, Y, and/or Z directions.

In general, the mechanical sensors 46 of a non-electrical motiondetermination system 44 detect the mechanical, i.e., physical, motion ofthe organ or organs of interest via one or more of the means listedabove. For example, internal movement, such as a heart beat orrespiration, may create mechanical motion detectable by one or moremechanical sensors 46 disposed on the skin of the patient 14 aspressure, displacement, acceleration, and so forth. In this manner,internal motion of one or more internal organs may be detected,measured, and represented, either as a map of surface motion or as a mapof internal motion. Such a map of surface or internal motion may becombined with other sensor or image data to generate a fusion imagerepresenting the various measured characteristics, such as structure,acceleration displacement, and/or electrical activity.

Image-based and sensor-based motion detection and measurement using thevarious systems and components of FIG. 1, is described in detail withreference to FIG. 2. Detection and/or measurement of the motion of oneor more organs may occur via a variety of processes and at differenttimes prior to, during or after the imaging process. In particular,image-based detection and/or measurement of organ motion, as depicted byblock 50, and/or sensor-based detection and/or measurement of organmotion, as depicted at block 52, may occur at suitable times within theprocess. Due to this time independence, the various motion measurementand/or detection techniques may be performed in a variety of orders orconcurrently, depending on the constraints of the technique, with theresulting motion data representing the data available at any given time.For example, within the category of image-based techniques 50, motionmay be detected and/or measured by using pre-acquisition motion data 54,as determined at block 56, and/or by using acquisition motion data 58,as determined at block 60.

Examples of pre-acquisition techniques for detecting or measuring motionof one or more organs, as depicted at step 56, include the use ofnavigator echoes in MRI, the use of scout images in CT, and the use offluoroscopic images in other X-ray contexts. Pre-acquisition motiondetection and measurement typically involves determining the position ofthe organ or organs of interest by a pre-acquisition measurement usingthe imaging system 10. Subsequent image acquisition can then occurduring similar states of organ motion or subsequently acquired imagedata may be selected for processing and reconstruction based upon asimilar state of organ motion.

For example, in MRI, a navigator echo pulse is a quick MR pre-pulsesequence that measures the position of an organ, such as the diaphragm,before primary image data acquisition. The pre-pulse sequence images anarrow area perpendicular to the movement of the organ of interest,i.e., a vertical area for a diaphragm. The contrast of the movinginterface is typically high to permit easy automatic detection. Once thepre-acquisition motion data 54 has been acquired, the position of theinterface may be recorded and imaging data may be acquired or selectedbased on whether the position of the interface falls within a range ofpre-specified values determined from the pre-acquisition data 54. Usingthe navigator echo data, similar respiratory rates or other motionstates of the patient can be identified and used for motion estimation.Hence, the navigator echo technique may be used as a respiratory gatingtechnique that does not utilize additional sensing equipment, as the MRsystem itself provides the sensing.

Similarly, acquisition motion data 58, such as organ motion informationderived from the unreconstructed and/or reconstructed image domains, maybe used to determine the motion of one or more organs. The acquisitionmotion data 58 may be determined from one-dimensional, two-dimensional,or three-dimensional representations of the imaged region derived fromthe image data. For example, organ motion may be determined in theunreconstructed image domain after a segmentation or structureidentification step. Changes in the location of the segmented structureor region may be determined in sequential image date and equate to themotion of the organ or organs. In this manner, acquisition motion data58 may be used to determine motion for one or more organs in the fieldof view of the imager 12.

Likewise, within the category of sensor-based techniques 52, motion ofone or more organs may be determined using electrical motion data 62, asdetermined at block 64, and/or by using non-electrical motion data 66,as determined at block 68. The electrical motion data 62 may include ECGdata if the heart is an organ of interest, or impedance plethysmographydata if the lungs are an organ of interest. Electrical signals orproperties of other organs of interest may also comprise the electricalmotion data provided suitable electrical sensors 42 are disposed on thepatient 14.

In regard to non-electrical motion data 66, the motion of virtually anyorgan may generate mechanical or physical indicia that may be detectedor measured by suitable mechanical sensors 46 disposed on the skin ofthe patient 14. For example, accelerometers may comprise the mechanicalsensors 46 for measuring acceleration, and the respective displacement,resulting from the motion of an internal organ, such as the heart,lungs, stomach, liver, pancreas, and so on. Similarly, ultrasonicsensors, optical markers, strain gauges, and so forth may be deployed asmechanical sensors 46 suitable for measuring acceleration, displacement,velocity, pressure, and other non-electrical motion data 62 associatedwith one or more organs.

The aggregate motion data 70, i.e., the motion data for each organ forwhich motion was detected or measured and/or for each motion sensingmethodology employed, contains the multi-input motion data 72 ofinterest for the imaging process. Aspects of the aggregate motion data70 may be combined and/or separated for each organ of interest or forthe different motion sensing methodologies, as depicted at block 74, toderive the multi-input motion data 72 relevant for the organs or organsof interest at a given time or point in the process. The combinationand/or separation procedure depicted may depend on the number of organsof interest, the techniques employed to measure motion, the coveragearea of the imaging modality, the processing techniques to be employed,i.e., prospective and/or retrospective, and so forth. For example, wherethe motion of an organ is measured or detected by multiple motionsensing methodologies, the motion information may be combined to derivea more accurate motion characterization of the organ at a given time.Similarly, where a motion sensing methodology detects the motion of morethan one organ, the information associated with each organ may beseparated to better characterize the motion of the individual organs ata given time. The result of the combination and/or separation procedure74 is one or more sets of multi-input motion data 72 which may be usedfor motion compensation in respective images and/or may be subsequentlyanalyzed to obtain prospective or retrospective gating intervals forimage acquisition and processing.

In this manner, the multi-input motion data 72 may comprise motion datafor the organ of interest derived from multiple sources, such assensor-based or image data-based sources. The multi-input motion datamay also comprise motion data for proximate organs, which contribute tothe overall motion of the organ of interest, derived from one or moresources, including possibly a source used to measure the motion of theorgan of interest. The multi-input nature of the multi-input motiondata, therefore, may encompass either the presence of motion data formore than one organ, the presence of multiple sources of motion data foran organ, or the presence of both multi-organ and multi-source motiondata.

Furthermore, positional information may be taken into account, such asduring data acquisition, combination and/or separation, or duringsubsequent processing, to generate the multi-input motion data 72. Forexample, an initial determination may be made whether the coverage areaof the sensors 36 or of the imager 12 is sufficient to cover the desiredregion of interest. If the coverage area is sufficient, motion detectionand/or measurement and image processing may proceed as discussed above.

However, if the coverage area is insufficient, such as when scans orimages are being taken of the torso from the neck to the abdomen or ofthe whole body, positional sensors may be employed. The positionalsensors may provide information concerning the region being currentlyscanned so that other data acquisition activities may be activated ordeactivated accordingly. For example, when positional sensors areemployed, the sensors 36 in the area being scanned by the imager 12 maybe activated while sensors 36 outside of the scanned area may remaininactive. In this manner, unnecessary or redundant data is not acquired.As one of ordinary skill in the art will appreciate, the number ofpositional sensors may vary depending on the application. Alternatively,the sensors 36 may all be active during the image data acquisition. Thepositional information obtained by the positional sensors, however, maybe employed during the combination and/or separation step 74, or duringsubsequent processes, to discard unnecessary image or motion data or tootherwise account for the positional information during imaging.

An example of a set of multi-input motion data 72 is depicted in FIG. 3.As depicted in FIG. 3, the multi-input motion data 72 includes threewaveforms, two cardiac motion waveforms 80 and a pulmonary motionwaveform 82. The cardiac waveforms include an ECG 84 and a mechanicalcardiogram (MCG) 86, such as may be derived using one or more mechanicalsensors 46 disposed on the skin that measure cardiac acceleration and/ordisplacement. The pulmonary motion waveform 82 may be derived byelectrical means, such as impedance plethysmography, or bynon-electrical means, such as by the acceleration or displacementmeasured by one or more mechanical sensors 46. Indeed, the samemechanical sensors 46 may be used to measure both heart and lung motionwith the motion data associated with each organ being separated to formthe multi-input motion data 72.

In the context of gating techniques, the multi-input motion data 72 maybe processed to extract one or more quiescent periods 88 for the one ormore organs of interest, as depicted at step 90 of FIG. 4. Referringonce again to FIG. 3, this extraction process can be visualized withreference to the cardiac waveforms 80 and the pulmonary waveform 82. Forexample, referring to the ECG waveform 84, an interval of minimal motion92 can be distinguished from the ECG 84 between the T wave and thesubsequent P wave. Similarly, an interval of minimal motion 94 can bedistinguished from the MCG 86 roughly coincident with the interval 92determined from the ECG 84. A cardiac interval of minimal motion 96which reflects the interval common to the ECG interval 92 and the MCGinterval 94 can thereby be derived from the cardiac waveforms 80.Similarly, a pulmonary interval of minimal motion 98 can be derived fromthe pulmonary waveform 82.

A quiescent period 88 that reflects the interval common to the cardiacinterval of minimal motion 96 and the pulmonary interval of minimalmotion 98 may thereby be derived. In the context of the present example,the quiescent period 88 represents an interval of minimal motion for allof the organs represented by the multi-input motion data 72. As one ofordinary skill will appreciate, additional quiescent periods 88 may besimilarly derived. Furthermore, additional motion data for these organsand/or for other or additional organs may be included within themulti-input motion data 72. In particular, the generation and/orprocessing of the multi-input motion data set 72, such as in theseparation/combination step 78 and/or in the extraction step 90, can beperformed for each individual organ separately, in series, in parallel,or in any order for use in subsequent processes and analyses.

As a validation step or as an additional step in the process, the organmotion from a second source, such as from motion computed in eitherone-dimension or two-dimensions from the unreconstructed orreconstructed acquisition image data may be used for validation. In thismanner, organ motion derived from the acquisition image data can besynergistically used to determine one or more quiescent periods 88 or tovalidate the quiescent periods 88 determined by sensor-based and/orpre-acquisition image data-based techniques, as depicted at decisionblock 100. In addition, the one or more quiescent periods 88 may betested for validity based on organ specific knowledge 102 pertaining tothe one or more organs of interest, such as may be obtained from ageneral physiological reference work or database, or based on patientspecific factors, such as patient history. If the quiescent period 88 isdetermined not to be valid based on the image-based motion informationand/or the organ or patient specific knowledge, an operator may bealerted that no quiescent period 88 can be extracted, as depicted atblock 104, and acquisition or processing may be terminated or modified.

If the one or more quiescent periods 88 are determined to be valid, thevalidated quiescent periods 88 may be used to determine motioncompensation factors 106, as depicted at step 108. Determination of oneor more motion compensation factors 106 may involve modeling theanticipated motion, based on the multi-input motion data 72 and/orquiescent periods 88, and calculating factors that compensate for themotion. Motion modeling can be accomplished either using the datadirectly or using a priori information about the moving organ. Whenmotion is computed directly from the data, motion compensation factors106 may be derived by an iterative algorithm trying to optimize criteriain multiple domains including the spatial and transform domains. Whenprior information is available, non-iterative methods can be used fordetermination of motion compensation factors 106.

Gating points 110 may be derived from the validated quiescent periods 88at step 108 instead of, or in addition to, motion compensation factors106. The gating points 110 typically describe the points in time fromwhich acquired data is selected, for retrospective gating, or duringwhich data acquisition occurs, for prospective gating. For example,referring to FIG. 3, gating points 110 may be selected which coincidewith the beginning and end of the quiescent period 88 such that imagedata is acquired or selected at the beginning of the quiescent period 88and is not selected or acquired after the end of the quiescent period88. In this manner, image data is acquired or selected which correspondsto an interval of low or no motion for the organ of interest, such asthe heart or lungs in this example. Reconstructed images of the organ ororgans of interest, therefore, should be less susceptible tomotion-related artifacts.

Though the preceding discussion relates the derivation and use of one ormore quiescent periods 88, other periods of interest may be derived and,therefore, substituted for the quiescent period 88. For example, asdiscussed above, the desired period of interest may correspond to aminimized phase of motion for one organ, while a second organ is at aphase of motion in which motion is not minimized. In this manner, theeffects of the motion of a proximate organ may be minimized whileemphasizing the desired phase of motion, such as the initiation of acontraction, of the second organ. Similarly, motion signatures may beused to select the desired phase of motion. For example, inconsistenciesbetween the multiple motion inputs may indicate a problem condition thatmay be of interest. For example, electrical data indicative of acontraction acquired concurrently with mechanical data that does notdemonstrate the expected contraction may represent a period of interest.Such signatures may of course vary depending on the event of interestand upon the parameters being measured.

While the preceding discussion and example relates to living complexsystems and medical imaging in particular, one of ordinary skill in theart will appreciate that the technique may be employed in the context ofother complex systems, such as mechanical systems. For example, complexmechanical systems may include numerous moving components disposed onthe interior of the system. As with a living organism, the motion ofthese various components may contribute to the overall motion of aparticular component of interest. To the extent that it is desirable toknow the overall motion of the component of interest or to identifyparticular periods of interest, such as motion signatures that mayindicate potential failure, the present technique may be employed.

For example, mechanical sensors 46 that measure displacement,acceleration, and so forth, may be useful indicators of vibration,rotation, frequency, and so forth, associated with one or more internalcomponents of a complex mechanical system. Similarly, electrical sensors42 may measure, electrical and/or magnetic field strength, resistance,impedance, and so forth, associated with one or more internal componentsof a complex mechanical system. Similarly, non-invasive imagingtechniques may be employed to acquire image data that may be processedto provide motion data inputs to the multi-input process discussedherein. By means of these various motion determining methodologies, amulti-input set of motion data may be generated and processed, asdescribed with respect to FIGS. 2 and 4, to identify a period ofinterest associated with one or more of the dynamic components.

Based upon the identified period of interest, the complex system may beoperated or serviced, depending on the significance of the period. Forexample, a period of interest selected based upon a multi-inputsignature associated with a pending failure of fault condition, mayindicate the need for servicing. In this manner, maintenance of a systemor performance of a component of a system may be determined usingmulti-input motion data.

While the invention may be susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and have been described in detail herein.However, it should be understood that the invention is not intended tobe limited to the particular forms disclosed. Rather, the invention isto cover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention as defined by the followingappended claims.

1. A method for identifying one or more periods of minimal motion for aheart, comprising the steps of: acquiring at least one set of electricaldata representative of cardiac motion and at least one set ofnon-electrical data representative of cardiac motion; generating a setof multi-input motion data comprising the sets of electrical andnon-electrical data; and extracting one or more periods of minimalmotion for the heart from the set of multi-input motion data.
 2. Themethod, as recited in claim 1, wherein the set of multi-input motiondata further comprises one or more sets of motion data for one or moreproximate organs.
 3. The method, as recited in claim 1, wherein the stepof acquiring comprises measuring a set of electrical data representativeof cardiac motion using electrical sensors.
 4. The method, as recited inclaim 1, wherein the step of acquiring comprises measuring a set ofnon-electrical data representative of cardiac motion using mechanicalsensors.
 5. The method, as recited in claim 1, wherein the step ofacquiring comprises measuring the motion of the heart from one or moreimages.
 6. The method, as recited in claim 5, wherein the one or moreimages are derived from one of pre-acquisition image data,unreconstructed acquisition image data, and reconstructed acquisitionimage data.
 7. The method as recited in claim 1, further comprising thestep of determining a set of motion compensation factors from the one ormore periods of minimal motion.
 8. The method as recited in claim 1,further comprising the step of determining two or more gating pointsfrom the one or more periods of minimal motion.
 9. The method as recitedin claim 1, further comprising the step of validating the one or moreperiods of minimal motion.
 10. A method for identifying one or moreperiods of minimal motion, comprising the steps of: acquiring at leastone set of non-electrical data representative of cardiac motion and oneor more sets of data representative of respiratory motion; generating aset of multi-input motion data comprising the set non-electrical datarepresentative of cardiac motion and the one or more sets of motion datarepresentative of respiratory motion; and extracting one or more periodsof minimal motion for one of a heart and a respiratory organ from theset of multi-input motion data.
 11. The method, as recited in claim 10,wherein the one or more sets of data representative of respiratorymotion comprise at least one of a set of electrical data representativeof respiratory motion and a set of non-electrical data representative ofrespiratory motion.
 12. The method, as recited in claim 10, wherein theset of multi-input motion data further comprises one or more sets ofmotion data for one or more proximate organs.
 13. The method, as recitedin claim 10, wherein the step of acquiring comprises measuring a set ofnon-electrical data representative of cardiac motion using mechanicalsensors.
 14. The method, as recited in claim 10, wherein the step ofacquiring comprises measuring a set of electrical data representative ofrespiratory motion using electrical sensors.
 15. The method, as recitedin claim 10, wherein the step of acquiring comprises measuring a set ofnon-electrical data representative of respiratory motion usingmechanical sensors.
 16. The method, as recited in claim 10, wherein thestep of acquiring comprises measuring one of cardiac motion andrespiratory motion from one or more images.
 17. The method, as recitedin claim 16, wherein the one or more images are derived from one ofpre-acquisition image data, unreconstructed acquisition image data, andreconstructed acquisition image data.
 18. The method as recited in claim10, further comprising the step of determining a set of motioncompensation factors from the one or more periods of minimal motion. 19.The method as recited in claim 10, further comprising the step ofdetermining two or more gating points from the one or more periods ofminimal motion.
 20. The method as recited in claim 1, further comprisingthe step of validating the one or more periods of minimal motion.
 21. Amethod for identifying one or more periods of minimal motion, comprisingthe steps of: acquiring at least one set of electrical datarepresentative of cardiac motion, at least one set of non-electricaldata representative of cardiac motion, and one or more sets of datarepresentative of respiratory motion; generating a set of multi-inputmotion data comprising the set of electrical data representative ofcardiac motion, the set non-electrical data representative of cardiacmotion, and the one or more sets of motion data representative ofrespiratory motion; and extracting one or more periods of minimal motionfor one of a heart and a respiratory organ from the set of multi-inputmotion data.
 22. The method, as recited in claim 21, wherein the one ormore sets of data representative of respiratory motion comprise at leastone of a set of electrical data representative of respiratory motion anda set of non-electrical data representative of respiratory motion. 23.The method, as recited in claim 21, wherein the set of multi-inputmotion data further comprises one or more sets of motion data for one ormore proximate organs.
 24. The method, as recited in claim 21, whereinthe step of acquiring comprises measuring a set of electrical datarepresentative of cardiac motion using electrical sensors.
 25. Themethod, as recited in claim 21, wherein the step of acquiring comprisesmeasuring a set of non-electrical data representative of cardiac motionusing mechanical sensors.
 26. The method, as recited in claim 21,wherein the step of acquiring comprises measuring a set of electricaldata representative of respiratory motion using electrical sensors. 27.The method, as recited in claim 21, wherein the step of acquiringcomprises measuring a set of non-electrical data representative ofrespiratory motion using mechanical sensors.
 28. The method, as recitedin claim 21, wherein the step of acquiring comprises measuring one ofcardiac motion and respiratory motion from one or more images.
 29. Themethod, as recited in claim 28, wherein the one or more images arederived from one of pre-acquisition image data, unreconstructedacquisition image data, and reconstructed acquisition image data. 30.The method as recited in claim 21, further comprising the step ofdetermining a set of motion compensation factors from the one or moreperiods of minimal motion.
 31. The method as recited in claim 21,further comprising the step of determining two or more gating pointsfrom the one or more periods of minimal motion.
 32. The method asrecited in claim 21, further comprising the step of validating the oneor more periods of minimal motion.
 33. A computer program, provided onone or more computer readable media, for identifying one or more periodsof minimal motion for a heart, comprising: a routine for acquiring atleast one set of electrical data representative of cardiac motion and atleast one set of non-electrical data representative of cardiac motion; aroutine for generating a set of multi-input motion data comprising thesets of electrical and non-electrical data; and, a routine forextracting one or more periods of minimal motion for the heart from theset of multi-input motion data.
 34. The computer program, as recited inclaim 33, the routine for generating the set of multi-input motion dataincludes one or more sets of motion data for one or more proximateorgans in the set of multi-input motion data.
 35. The computer program,as recited in claim 33, wherein the routine for acquiring measures a setof electrical data representative of cardiac motion using electricalsensors.
 36. The computer program, as recited in claim 33, wherein theroutine for acquiring measures a set of non-electrical datarepresentative of cardiac motion using mechanical sensors.
 37. Thecomputer program, as recited in claim 33, wherein the routine foracquiring measures the motion of the heart from one or more images. 38.The computer program, as recited in claim 37, wherein the one or moreimages are derived from one of pre-acquisition image data,unreconstructed acquisition image data, and reconstructed acquisitionimage data.
 39. The computer program, as recited in claim 33, comprisinga routine for determining a set of motion compensation factors from theone or more periods of minimal motion.
 40. The computer program, asrecited in claim 33, comprising a routine for determining two or moregating points from the one or more periods of minimal motion.
 41. Thecomputer program, as recited in claim 33, comprising a routine forvalidating the one or more periods of minimal motion.
 42. A computerprogram, provided on one or more computer readable media, foridentifying one or more periods of minimal motion, comprising: a routinefor acquiring at least one set of non-electrical data representative ofcardiac motion and one or more sets of data representative ofrespiratory motion; a routine for generating a set of multi-input motiondata comprising the set non-electrical data representative of cardiacmotion and the one or more sets of motion data representative ofrespiratory motion; and a routine for extracting one or more periods ofminimal motion for one of a heart and a respiratory organ from the setof multi-input motion data.
 43. The computer program, as recited inclaim 42, wherein the one or more sets of data representative ofrespiratory motion comprise at least one of a set of electrical datarepresentative of respiratory motion and a set of non-electrical datarepresentative of respiratory motion.
 44. The computer program, asrecited in claim 42, wherein the routine for generating the set ofmulti-input motion data includes one or more sets of motion data for oneor more proximate organs in the set of multi-input motion data.
 45. Thecomputer program, as recited in claim 42, wherein the routine foracquiring measures a set of non-electrical data representative ofcardiac motion using mechanical sensors.
 46. The computer program, asrecited in claim 42, wherein the routine for acquiring measures a set ofelectrical data representative of respiratory motion using electricalsensors.
 47. The computer program, as recited in claim 42, wherein theroutine for acquiring measures a set of non-electrical datarepresentative of respiratory motion using mechanical sensors.
 48. Thecomputer program, as recited in claim 42, wherein the routine foracquiring measures one of cardiac motion and respiratory motion from oneor more images.
 49. The computer program, as recited in claim 48,wherein the one or more images are derived from one of pre-acquisitionimage data, unreconstructed acquisition image data, and reconstructedacquisition image data.
 50. The computer program, as recited in claim42, comprising a routine for determining a set of motion compensationfactors from the one or more periods of minimal motion.
 51. The computerprogram, as recited in claim 42, comprising a routine for determiningtwo or more gating points from the one or more periods of minimalmotion.
 52. The computer program, as recited in claim 42, comprising aroutine for validating the one or more periods of minimal motion.
 53. Acomputer program, provided on one or more computer readable media, foridentifying one or more periods of minimal motion, comprising: a routinefor acquiring at least one set of electrical data representative ofcardiac motion, at least one set of non-electrical data representativeof cardiac motion, and one or more sets of motion data representative ofrespiratory motion; a routine for generating a set of multi-input motiondata comprising the set of electrical data representative of cardiacmotion, the set non-electrical data representative of cardiac motion,and the one or more sets of data representative of respiratory motion;and a routine for extracting one or more periods of minimal motion forone of a heart and a respiratory organ from the set of multi-inputmotion data.
 54. The computer program, as recited in claim 53, whereinthe one or more sets of data representative of respiratory motioncomprise at least one of a set of electrical data representative ofrespiratory motion and a set of non-electrical data representative ofrespiratory motion.
 55. The computer program, as recited in claim 53,wherein the routine for generating the set of multi-input motion dataincludes one or more sets of motion data for one or more proximateorgans in the set of multi-input motion data.
 56. The computer program,as recited in claim 53, wherein the routine for acquiring measures a setof electrical data representative of cardiac motion using electricalsensors.
 57. The computer program, as recited in claim 53, wherein theroutine for acquiring measures a set of non-electrical datarepresentative of cardiac motion using mechanical sensors.
 58. Thecomputer program, as recited in claim 53, wherein the routine foracquiring measures a set of electrical data representative ofrespiratory motion using electrical sensors.
 59. The computer program,as recited in claim 53, wherein the routine for acquiring measures a setof non-electrical data representative of respiratory motion usingmechanical sensors.
 60. The computer program, as recited in claim 53,wherein the routine for acquiring measures one of cardiac motion andrespiratory motion from one or more images.
 61. The computer program, asrecited in claim 60, wherein the one or more images are derived from oneof pre-acquisition image data, unreconstructed acquisition image data,and reconstructed acquisition image data.
 62. The computer program, asrecited in claim 53, comprising a routine for determining a set ofmotion compensation factors from the one or more periods of minimalmotion.
 63. The computer program, as recited in claim 53, comprising aroutine for determining two or more gating points from the one or moreperiods of minimal motion.
 64. The computer program, as recited in claim53, comprising a routine for validating the one or more periods ofminimal motion.
 65. An imaging system, comprising: means for acquiringat least one set of electrical data representative of cardiac motion andat least one set of non-electrical data representative of cardiacmotion; means for generating a set of multi-input motion data comprisingthe sets of electrical and non-electrical data; and means for extractingone or more periods of minimal motion for the heart from the set ofmulti-input motion data.
 66. An imaging system, comprising: means foracquiring at least one set of non-electrical data representative ofcardiac motion and one or more sets of data representative ofrespiratory motion; means for generating a set of multi-input motiondata comprising the set non-electrical data representative of cardiacmotion and the one or more sets of motion data representative ofrespiratory motion; and means for extracting one or more periods ofminimal motion for one of a heart and a respiratory organ from the setof multi-input motion data.
 67. An imaging system, comprising: means foracquiring at least one set of electrical data representative of cardiacmotion, at least one set of non-electrical data representative ofcardiac motion, and one or more sets of data representative ofrespiratory motion; means for generating a set of multi-input motiondata comprising the set of electrical data representative of cardiacmotion, the set non-electrical data representative of cardiac motion,the one or more sets of motion data representative of respiratorymotion; and means for extracting one or more periods of minimal motionfor one of a heart and a respiratory organ from the set of multi-inputmotion data.
 68. An imaging system, comprising: an imager configured togenerate a plurality of signals representative of a heart; dataacquisition circuitry configured to acquire the plurality of signals;data processing circuitry configured to receive the plurality ofsignals; system control circuitry configured to operate at least one ofthe imager and the data acquisition circuitry; an operator workstationconfigured to communicate with the system control circuitry and toreceive at least the processed plurality of signals from the dataprocessing circuitry; one or more sensor-based motion measurementsystems configured to measure electrical activity indicative of themotion of the heart; and one or more sensor-based motion measurementsystems configured to measure non-electrical activity indicative of themotion of the heart; wherein one or more of the data processingcircuitry and operator workstation are configured to extract one or moreperiods of minimal motion for the heart from a set of multi-input motiondata comprising at least a set of electrical data representative ofcardiac motion and a set of non-electrical data representative ofcardiac motion acquired by the respective sensor-based motionmeasurement systems.
 69. An imaging system, comprising: an imagerconfigured to generate a plurality of signals representative of at leastone of a heart and a respiratory organ; data acquisition circuitryconfigured to acquire the plurality of signals; data processingcircuitry configured to receive the plurality of signals; system controlcircuitry configured to operate at least one of the imager and the dataacquisition circuitry; an operator workstation configured to communicatewith the system control circuitry and to receive at least the processedplurality of signals from the data processing circuitry; one or moresensor-based motion measurement systems configured to measurenon-electrical activity indicative of the motion of the heart; and oneor more sensor-based motion measurement systems configured to measureelectrical or non-electrical activity indicative of the motion of therespiratory organ; wherein one or more of the data processing circuitryand operator workstation are configured to extract one or more periodsof minimal motion for one of the heart and the respiratory organ from aset of multi-input motion data comprising at least a set ofnon-electrical data representative of cardiac motion and a set ofelectrical or non-electrical data representative of respiratory motionacquired by the respective sensor-based motion measurement systems. 70.The imaging system as recited in claim 69, wherein a sensor-based motionmeasurement systems configured to measure non-electrical activityindicative of the motion of the heart and a sensor-based motionmeasurement systems configured to measure non-electrical activityindicative of the motion of the respiratory organ are the same.
 71. Animaging system, comprising: an imager configured to generate a pluralityof signals representative of at least one of a heart and a respiratoryorgan; data acquisition circuitry configured to acquire the plurality ofsignals; data processing circuitry configured to receive the pluralityof signals; system control circuitry configured to operate at least oneof the imager and the data acquisition circuitry; an operatorworkstation configured to communicate with the system control circuitryand to receive at least the processed plurality of signals from the dataprocessing circuitry; one or more sensor-based motion measurementsystems configured to measure electrical activity indicative of themotion of the heart; one or more sensor-based motion measurement systemsconfigured to measure non-electrical activity indicative of the motionof the heart; and one or more sensor-based motion measurement systemsconfigured to measure electrical or non-electrical activity indicativeof the motion of the respiratory organ; wherein one or more of the dataprocessing circuitry and operator workstation are configured to extractone or more periods of minimal motion for one of the heart and therespiratory organ from a set of multi-input motion data comprising atleast a set of electrical data representative of cardiac motion, atleast a set of non-electrical data representative of cardiac motion, anda set of electrical or non-electrical data representative of respiratorymotion acquired by the respective sensor-based motion measurementsystems.
 72. The imaging system as recited in claim 69, wherein asensor-based motion measurement systems configured to measurenon-electrical activity indicative of the motion of the heart and asensor-based motion measurement systems configured to measurenon-electrical activity indicative of the motion of the respiratoryorgan are the same.